Low-cost whole slide imaging system with single-shot autofocusing based on color-multiplexed illumination and deep learning

Abstract

Recent research on whole slide imaging (WSI) has greatly promoted the development of digital pathology. However, accurate autofocusing is still the main challenge for WSI acquisition and automated digital microscope. To address this problem, this paper describes a low cost WSI system and proposes a fast, robust autofocusing method based on deep learning. We use a programmable LED array for sample illumination. Before the brightfield image acquisition, we turn on a red and a green LED, and capture a color-multiplexed image, which is fed into a neural network for defocus distance estimation. After the focus tracking process, we employ a low-cost DIY adaptor to digitally adjust the photographic lens instead of the mechanical stage to perform axial position adjustment, and acquire the in-focus image under brightfield illumination. To ensure the calculation speed and image quality, we build a network model based on a ‘light weight’ backbone network architecture-MobileNetV3. Since the color-multiplexed coherent illuminated images contain abundant information about the defocus orientation, the proposed method enables high performance of autofocusing. Experimental results show that the proposed method can accurately predict the defocus distance of various types of samples and has good generalization ability for new types of samples. In the case of using GPU, the processing time for autofocusing is less than 0.1 second for each field of view, indicating that our method can further speed up the acquisition of whole slide images.